Overview

Brought to you by YData

Dataset statistics

Number of variables38
Number of observations180
Missing cells40
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory202.9 KiB
Average record size in memory1.1 KiB

Variable types

Numeric17
DateTime2
Text3
Categorical13
Boolean3

Alerts

Date Time has constant value "2025-07-03 00:00:00" Constant
Date has constant value "2025-07-03 00:00:00" Constant
Curr_Price is highly overall correlated with Day1_Pred and 9 other fieldsHigh correlation
Day1_Pred is highly overall correlated with Curr_Price and 8 other fieldsHigh correlation
Day3_Pred is highly overall correlated with Curr_Price and 8 other fieldsHigh correlation
Day7_Pred is highly overall correlated with Curr_Price and 8 other fieldsHigh correlation
Sell is highly overall correlated with made_profit_act_day_1High correlation
actual_day1_close is highly overall correlated with Curr_Price and 9 other fieldsHigh correlation
actual_day3_close is highly overall correlated with Curr_Price and 8 other fieldsHigh correlation
actual_day7_close is highly overall correlated with Curr_Price and 8 other fieldsHigh correlation
closest_day1_price is highly overall correlated with Curr_Price and 9 other fieldsHigh correlation
closest_day3_price is highly overall correlated with Curr_Price and 8 other fieldsHigh correlation
closest_day7_price is highly overall correlated with Curr_Price and 8 other fieldsHigh correlation
day1_act_error is highly overall correlated with day1_min_error and 4 other fieldsHigh correlation
day1_min_error is highly overall correlated with day1_act_error and 4 other fieldsHigh correlation
day3_act_error is highly overall correlated with day1_act_error and 4 other fieldsHigh correlation
day3_min_error is highly overall correlated with day1_act_error and 4 other fieldsHigh correlation
day7_act_error is highly overall correlated with day1_act_error and 4 other fieldsHigh correlation
day7_min_error is highly overall correlated with day1_act_error and 4 other fieldsHigh correlation
made_profit_act_day_1 is highly overall correlated with Sell and 1 other fieldsHigh correlation
made_profit_act_day_3 is highly overall correlated with made_profit_act_day_7 and 3 other fieldsHigh correlation
made_profit_act_day_7 is highly overall correlated with made_profit_act_day_3 and 3 other fieldsHigh correlation
made_profit_could_day_3 is highly overall correlated with made_profit_act_day_3 and 3 other fieldsHigh correlation
made_profit_could_day_7 is highly overall correlated with made_profit_act_day_3 and 4 other fieldsHigh correlation
missed_opportunity_day7 is highly overall correlated with Curr_Price and 2 other fieldsHigh correlation
profit_1 is highly overall correlated with made_profit_act_day_1High correlation
profit_3 is highly overall correlated with made_profit_act_day_3 and 3 other fieldsHigh correlation
profit_7 is highly overall correlated with made_profit_act_day_7 and 2 other fieldsHigh correlation
missed_opportunity_day7 is highly imbalanced (69.0%) Imbalance
closest_day1_price has 5 (2.8%) missing values Missing
actual_day1_close has 5 (2.8%) missing values Missing
day1_min_error has 5 (2.8%) missing values Missing
day1_act_error has 5 (2.8%) missing values Missing
closest_day3_price has 5 (2.8%) missing values Missing
actual_day3_close has 5 (2.8%) missing values Missing
day3_min_error has 5 (2.8%) missing values Missing
day3_act_error has 5 (2.8%) missing values Missing
Unnamed: 0 is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
Curr_Price has 5 (2.8%) zeros Zeros
day1_min_error has 11 (6.1%) zeros Zeros
day3_min_error has 2 (1.1%) zeros Zeros

Reproduction

Analysis started2025-07-13 14:44:31.926597
Analysis finished2025-07-13 14:45:02.586251
Duration30.66 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

Uniform  Unique 

Distinct180
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.633333
Minimum0
Maximum189
Zeros1
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-07-13T20:15:02.674262image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.95
Q144.75
median90.5
Q3139.5
95-th percentile180.05
Maximum189
Range189
Interquartile range (IQR)94.75

Descriptive statistics

Standard deviation55.571978
Coefficient of variation (CV)0.5999134
Kurtosis-1.2106867
Mean92.633333
Median Absolute Deviation (MAD)47.5
Skewness0.068600089
Sum16674
Variance3088.2447
MonotonicityStrictly increasing
2025-07-13T20:15:02.814299image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
0.6%
1 1
 
0.6%
2 1
 
0.6%
3 1
 
0.6%
4 1
 
0.6%
5 1
 
0.6%
6 1
 
0.6%
7 1
 
0.6%
8 1
 
0.6%
9 1
 
0.6%
Other values (170) 170
94.4%
ValueCountFrequency (%)
0 1
0.6%
1 1
0.6%
2 1
0.6%
3 1
0.6%
4 1
0.6%
5 1
0.6%
6 1
0.6%
7 1
0.6%
8 1
0.6%
9 1
0.6%
ValueCountFrequency (%)
189 1
0.6%
188 1
0.6%
187 1
0.6%
186 1
0.6%
185 1
0.6%
184 1
0.6%
183 1
0.6%
182 1
0.6%
181 1
0.6%
180 1
0.6%

Date Time
Date

Constant 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
Minimum2025-07-03 00:00:00
Maximum2025-07-03 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-13T20:15:02.924915image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:15:03.018533image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Stock
Text

Distinct72
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Memory size12.1 KiB
2025-07-13T20:15:03.182734image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length20
Median length4
Mean length4.0944444
Min length2

Characters and Unicode

Total characters737
Distinct characters43
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)18.3%

Sample

1st rowNVDA
2nd rowAMZN
3rd rowTSLA
4th rowAMD
5th rowMETA
ValueCountFrequency (%)
intu 8
 
4.4%
docu 7
 
3.8%
ntes 7
 
3.8%
klac 6
 
3.3%
mrvl 6
 
3.3%
asml 6
 
3.3%
adp 6
 
3.3%
aapl 5
 
2.7%
rost 5
 
2.7%
idxx 5
 
2.7%
Other values (64) 122
66.7%
2025-07-13T20:15:03.482493image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 66
 
9.0%
S 55
 
7.5%
C 50
 
6.8%
T 47
 
6.4%
L 46
 
6.2%
N 46
 
6.2%
M 45
 
6.1%
D 42
 
5.7%
I 37
 
5.0%
O 35
 
4.7%
Other values (33) 268
36.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 687
93.2%
Lowercase Letter 47
 
6.4%
Space Separator 3
 
0.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 66
 
9.6%
S 55
 
8.0%
C 50
 
7.3%
T 47
 
6.8%
L 46
 
6.7%
N 46
 
6.7%
M 45
 
6.6%
D 42
 
6.1%
I 37
 
5.4%
O 35
 
5.1%
Other values (16) 218
31.7%
Lowercase Letter
ValueCountFrequency (%)
e 6
12.8%
o 5
10.6%
a 5
10.6%
c 4
8.5%
r 4
8.5%
h 3
 
6.4%
i 3
 
6.4%
n 3
 
6.4%
t 3
 
6.4%
g 2
 
4.3%
Other values (6) 9
19.1%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 734
99.6%
Common 3
 
0.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 66
 
9.0%
S 55
 
7.5%
C 50
 
6.8%
T 47
 
6.4%
L 46
 
6.3%
N 46
 
6.3%
M 45
 
6.1%
D 42
 
5.7%
I 37
 
5.0%
O 35
 
4.8%
Other values (32) 265
36.1%
Common
ValueCountFrequency (%)
3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 737
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 66
 
9.0%
S 55
 
7.5%
C 50
 
6.8%
T 47
 
6.4%
L 46
 
6.2%
N 46
 
6.2%
M 45
 
6.1%
D 42
 
5.7%
I 37
 
5.0%
O 35
 
4.7%
Other values (33) 268
36.4%

Curr_Price
Real number (ℝ)

High correlation  Zeros 

Distinct66
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean269.8969
Minimum0
Maximum1297.1801
Zeros5
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-07-13T20:15:03.610084image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8.9527498
Q176.389999
median133.615
Q3315.185
95-th percentile924.65997
Maximum1297.1801
Range1297.1801
Interquartile range (IQR)238.795

Descriptive statistics

Standard deviation295.56812
Coefficient of variation (CV)1.0951149
Kurtosis1.5973332
Mean269.8969
Median Absolute Deviation (MAD)83.735001
Skewness1.5427188
Sum48581.443
Variance87360.514
MonotonicityNot monotonic
2025-07-13T20:15:03.817124image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
132.8600006 12
 
6.7%
780.7999878 8
 
4.4%
78.97499847 7
 
3.9%
794.9799805 6
 
3.3%
308.9599915 6
 
3.3%
75.16000366 6
 
3.3%
924.6599731 6
 
3.3%
0 5
 
2.8%
131.6499939 5
 
2.8%
213.5899963 5
 
2.8%
Other values (56) 114
63.3%
ValueCountFrequency (%)
0 5
2.8%
2.164999962 3
1.7%
3.494999886 1
 
0.6%
9.239999771 1
 
0.6%
11.19999981 1
 
0.6%
12.89000034 2
 
1.1%
13.07999992 1
 
0.6%
16.14999962 2
 
1.1%
18.57999992 1
 
0.6%
22.46999931 2
 
1.1%
ValueCountFrequency (%)
1297.180054 3
 
1.7%
987.4000244 1
 
0.6%
924.6599731 6
3.3%
794.9799805 6
3.3%
780.7999878 8
4.4%
722 1
 
0.6%
718.5999756 4
2.2%
561.1049805 1
 
0.6%
547 5
2.8%
514.1099854 2
 
1.1%

Buy
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
126 
1
54 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 126
70.0%
1 54
30.0%

Length

2025-07-13T20:15:03.946668image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-13T20:15:04.045291image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0 126
70.0%
1 54
30.0%

Most occurring characters

ValueCountFrequency (%)
0 126
70.0%
1 54
30.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 180
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 126
70.0%
1 54
30.0%

Most occurring scripts

ValueCountFrequency (%)
Common 180
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 126
70.0%
1 54
30.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 180
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 126
70.0%
1 54
30.0%

Sell
Categorical

High correlation 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
136 
1
44 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 136
75.6%
1 44
 
24.4%

Length

2025-07-13T20:15:04.151816image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-13T20:15:04.248443image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0 136
75.6%
1 44
 
24.4%

Most occurring characters

ValueCountFrequency (%)
0 136
75.6%
1 44
 
24.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 180
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 136
75.6%
1 44
 
24.4%

Most occurring scripts

ValueCountFrequency (%)
Common 180
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 136
75.6%
1 44
 
24.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 180
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 136
75.6%
1 44
 
24.4%

Hold
Categorical

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
121 
1
59 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 121
67.2%
1 59
32.8%

Length

2025-07-13T20:15:04.356020image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-13T20:15:04.454645image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0 121
67.2%
1 59
32.8%

Most occurring characters

ValueCountFrequency (%)
0 121
67.2%
1 59
32.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 180
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 121
67.2%
1 59
32.8%

Most occurring scripts

ValueCountFrequency (%)
Common 180
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 121
67.2%
1 59
32.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 180
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 121
67.2%
1 59
32.8%

Day1_Pred
Real number (ℝ)

High correlation 

Distinct125
Distinct (%)69.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean277.17194
Minimum2.05
Maximum1345
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-07-13T20:15:04.572204image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum2.05
5-th percentile16.8
Q181.125
median141.425
Q3325.5
95-th percentile828.825
Maximum1345
Range1342.95
Interquartile range (IQR)244.375

Descriptive statistics

Standard deviation295.7671
Coefficient of variation (CV)1.0670889
Kurtosis2.227002
Mean277.17194
Median Absolute Deviation (MAD)85.2
Skewness1.6571459
Sum49890.95
Variance87478.179
MonotonicityNot monotonic
2025-07-13T20:15:04.717272image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
320 4
 
2.2%
215 4
 
2.2%
36.5 4
 
2.2%
313 4
 
2.2%
99.5 3
 
1.7%
80 3
 
1.7%
140 3
 
1.7%
725 3
 
1.7%
230 3
 
1.7%
74.5 3
 
1.7%
Other values (115) 146
81.1%
ValueCountFrequency (%)
2.05 1
0.6%
2.5 2
1.1%
4 1
0.6%
9.5 1
0.6%
12 2
1.1%
13 2
1.1%
17 2
1.1%
19.3 1
0.6%
22.5 2
1.1%
24 1
0.6%
ValueCountFrequency (%)
1345 2
1.1%
1305 1
0.6%
1234.56 1
0.6%
1015 1
0.6%
960 1
0.6%
950 1
0.6%
930 2
1.1%
823.5 2
1.1%
814.64 2
1.1%
814.23 2
1.1%

closest_day1_price
Real number (ℝ)

High correlation  Missing 

Distinct87
Distinct (%)49.7%
Missing5
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean277.19179
Minimum2.0599999
Maximum1291.3766
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-07-13T20:15:04.888381image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum2.0599999
5-th percentile15.298499
Q178.835499
median137.47
Q3318
95-th percentile905.90503
Maximum1291.3766
Range1289.3166
Interquartile range (IQR)239.1645

Descriptive statistics

Standard deviation295.59294
Coefficient of variation (CV)1.0663842
Kurtosis1.4766122
Mean277.19179
Median Absolute Deviation (MAD)81.970001
Skewness1.5168568
Sum48508.563
Variance87375.184
MonotonicityNot monotonic
2025-07-13T20:15:05.037902image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
787.5300293 6
 
3.3%
74.68000031 6
 
3.3%
79.16999817 6
 
3.3%
134.1549988 5
 
2.8%
549.4899902 5
 
2.8%
295.9299927 4
 
2.2%
159.1600037 4
 
2.2%
98.85900116 4
 
2.2%
215.1900024 4
 
2.2%
922.7999878 4
 
2.2%
Other values (77) 127
70.6%
(Missing) 5
 
2.8%
ValueCountFrequency (%)
2.059999943 1
0.6%
2.119999886 2
1.1%
3.470000029 1
0.6%
9.31499958 1
0.6%
11.28499985 1
0.6%
12.46000004 1
0.6%
12.88000011 2
1.1%
16.33499908 2
1.1%
19.26499939 1
0.6%
22.37999916 2
1.1%
ValueCountFrequency (%)
1291.376587 3
1.7%
992.8931274 1
 
0.6%
922.7999878 4
2.2%
905.9050293 2
 
1.1%
792.9899902 4
2.2%
787.5300293 6
3.3%
783.8300171 2
 
1.1%
780.5700073 1
 
0.6%
778.8400269 1
 
0.6%
726.5050049 1
 
0.6%

actual_day1_close
Real number (ℝ)

High correlation  Missing 

Distinct66
Distinct (%)37.7%
Missing5
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean275.49703
Minimum2.105
Maximum1289.0699
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-07-13T20:15:05.184143image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum2.105
5-th percentile15.0705
Q177.650002
median136.97
Q3317.955
95-th percentile912.90002
Maximum1289.0699
Range1286.9649
Interquartile range (IQR)240.305

Descriptive statistics

Standard deviation294.70917
Coefficient of variation (CV)1.0697363
Kurtosis1.4980579
Mean275.49703
Median Absolute Deviation (MAD)81.510002
Skewness1.5216764
Sum48211.98
Variance86853.492
MonotonicityNot monotonic
2025-07-13T20:15:05.338657image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
783.5499878 8
 
4.4%
77.65000153 7
 
3.9%
133.0399933 7
 
3.9%
308.4899902 6
 
3.3%
784.9899902 6
 
3.3%
912.9000244 6
 
3.3%
71.55999756 6
 
3.3%
130.7700043 5
 
2.8%
545.0999756 5
 
2.8%
209.9400024 5
 
2.8%
Other values (56) 114
63.3%
ValueCountFrequency (%)
2.105000019 3
1.7%
3.410000086 1
 
0.6%
9.18999958 1
 
0.6%
11.02000046 1
 
0.6%
12.52000046 2
1.1%
12.75 1
 
0.6%
16.06500053 2
1.1%
19.25499916 1
 
0.6%
21.99500084 2
1.1%
23.62000084 1
 
0.6%
ValueCountFrequency (%)
1289.069946 3
 
1.7%
992.210022 1
 
0.6%
912.9000244 6
3.3%
784.9899902 6
3.3%
783.5499878 8
4.4%
736.4000244 1
 
0.6%
718.6400146 4
2.2%
555.2399902 1
 
0.6%
545.0999756 5
2.8%
505.480011 2
 
1.1%

day1_min_error
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct128
Distinct (%)73.1%
Missing5
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean3.8623968
Minimum0
Maximum68.417134
Zeros11
Zeros (%)6.1%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-07-13T20:15:05.486830image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.58537007
median1.908839
Q33.6805377
95-th percentile12.773671
Maximum68.417134
Range68.417134
Interquartile range (IQR)3.0951676

Descriptive statistics

Standard deviation9.1117451
Coefficient of variation (CV)2.3590909
Kurtosis32.714533
Mean3.8623968
Median Absolute Deviation (MAD)1.4338645
Skewness5.4646341
Sum675.91944
Variance83.023899
MonotonicityNot monotonic
2025-07-13T20:15:05.623359image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11
 
6.1%
0.7321566825 4
 
2.2%
0.08829519924 4
 
2.2%
1.024076726 3
 
1.7%
0.6483970431 3
 
1.7%
4.611153252 3
 
1.7%
29.1785224 2
 
1.1%
2.577824792 2
 
1.1%
0.7802354034 2
 
1.1%
0.541809321 2
 
1.1%
Other values (118) 139
77.2%
(Missing) 5
 
2.8%
ValueCountFrequency (%)
0 11
6.1%
2.211075414 × 10-61
 
0.6%
3.730755136 × 10-61
 
0.6%
0.001247739466 1
 
0.6%
0.001273169374 1
 
0.6%
0.00138067639 1
 
0.6%
0.003777127375 1
 
0.6%
0.005552348511 1
 
0.6%
0.005840289011 2
 
1.1%
0.008966867177 1
 
0.6%
ValueCountFrequency (%)
68.41713426 1
0.6%
62.18588164 1
0.6%
61.84257855 1
0.6%
29.1785224 2
1.1%
26.70515437 1
0.6%
17.92453467 2
1.1%
15.27377427 1
0.6%
11.70219746 1
0.6%
8.133682939 2
1.1%
7.431348209 1
0.6%

day1_act_error
Real number (ℝ)

High correlation  Missing 

Distinct136
Distinct (%)77.7%
Missing5
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean5.0784807
Minimum0.072125705
Maximum68.606981
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-07-13T20:15:05.758976image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0.072125705
5-th percentile0.34576574
Q11.5784387
median3.0263985
Q35.0503577
95-th percentile14.211567
Maximum68.606981
Range68.534855
Interquartile range (IQR)3.4719189

Descriptive statistics

Standard deviation9.024216
Coefficient of variation (CV)1.7769519
Kurtosis31.733308
Mean5.0784807
Median Absolute Deviation (MAD)1.5644354
Skewness5.3304903
Sum888.73412
Variance81.436475
MonotonicityNot monotonic
2025-07-13T20:15:05.895115image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.410211251 4
 
2.2%
1.461963081 4
 
2.2%
1.37544417 3
 
1.7%
2.990974646 3
 
1.7%
6.721590563 3
 
1.7%
4.338791225 2
 
1.1%
3.967840303 2
 
1.1%
2.917488991 2
 
1.1%
3.915514349 2
 
1.1%
3.359896978 2
 
1.1%
Other values (126) 148
82.2%
(Missing) 5
 
2.8%
ValueCountFrequency (%)
0.07212570535 1
0.6%
0.0768686386 1
0.6%
0.1225471519 1
0.6%
0.1435409934 1
0.6%
0.1710762644 1
0.6%
0.2337098997 1
0.6%
0.2375296912 1
0.6%
0.2450999137 1
0.6%
0.3457657374 2
1.1%
0.3713850856 1
0.6%
ValueCountFrequency (%)
68.60698056 1
0.6%
62.47562813 1
0.6%
62.13495555 1
0.6%
29.2831747 2
1.1%
27.1927836 1
0.6%
18.76484453 2
1.1%
17.30204983 1
0.6%
12.8870743 1
0.6%
9.052159981 1
0.6%
8.999444747 1
0.6%
Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
1
95 
0
85 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 95
52.8%
0 85
47.2%

Length

2025-07-13T20:15:06.023278image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-13T20:15:06.121822image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
1 95
52.8%
0 85
47.2%

Most occurring characters

ValueCountFrequency (%)
1 95
52.8%
0 85
47.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 180
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 95
52.8%
0 85
47.2%

Most occurring scripts

ValueCountFrequency (%)
Common 180
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 95
52.8%
0 85
47.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 180
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 95
52.8%
0 85
47.2%

made_profit_act_day_1
Categorical

High correlation 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
109 
1
71 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 109
60.6%
1 71
39.4%

Length

2025-07-13T20:15:06.228411image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-13T20:15:06.325067image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0 109
60.6%
1 71
39.4%

Most occurring characters

ValueCountFrequency (%)
0 109
60.6%
1 71
39.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 180
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 109
60.6%
1 71
39.4%

Most occurring scripts

ValueCountFrequency (%)
Common 180
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 109
60.6%
1 71
39.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 180
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 109
60.6%
1 71
39.4%

Day3_Pred
Real number (ℝ)

High correlation 

Distinct143
Distinct (%)79.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean282.07156
Minimum2
Maximum1370
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-07-13T20:15:06.434651image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile17.8
Q182
median146.855
Q3333.8025
95-th percentile852.825
Maximum1370
Range1368
Interquartile range (IQR)251.8025

Descriptive statistics

Standard deviation300.75803
Coefficient of variation (CV)1.0662473
Kurtosis2.2224381
Mean282.07156
Median Absolute Deviation (MAD)85.65
Skewness1.6568991
Sum50772.88
Variance90455.391
MonotonicityNot monotonic
2025-07-13T20:15:06.574275image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.2 4
 
2.2%
80 3
 
1.7%
235 3
 
1.7%
145 3
 
1.7%
325 3
 
1.7%
220 3
 
1.7%
82 3
 
1.7%
165 2
 
1.1%
2.8 2
 
1.1%
22.8 2
 
1.1%
Other values (133) 152
84.4%
ValueCountFrequency (%)
2 1
0.6%
2.8 2
1.1%
4.5 1
0.6%
9.8 1
0.6%
11 1
0.6%
13 1
0.6%
13.5 1
0.6%
14 1
0.6%
18 2
1.1%
20.1 1
0.6%
ValueCountFrequency (%)
1370 2
1.1%
1310 1
0.6%
1267.89 1
0.6%
1030 1
0.6%
980 1
0.6%
970 1
0.6%
935 2
1.1%
848.5 1
0.6%
845.5 1
0.6%
844.19 1
0.6%

closest_day3_price
Real number (ℝ)

High correlation  Missing 

Distinct85
Distinct (%)48.6%
Missing5
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean277.87582
Minimum2.26
Maximum1288.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-07-13T20:15:06.712500image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum2.26
5-th percentile15.39891
Q179.489998
median140.4791
Q3321.13995
95-th percentile915.51001
Maximum1288.25
Range1285.99
Interquartile range (IQR)241.64996

Descriptive statistics

Standard deviation296.08953
Coefficient of variation (CV)1.0655462
Kurtosis1.4491427
Mean277.87582
Median Absolute Deviation (MAD)83.440903
Skewness1.5136796
Sum48628.268
Variance87669.008
MonotonicityNot monotonic
2025-07-13T20:15:06.854056image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
131.0500031 7
 
3.9%
73.38500214 6
 
3.3%
79.77999878 6
 
3.3%
308.6124878 6
 
3.3%
782.625 6
 
3.3%
543.3300171 5
 
2.8%
299.3599854 4
 
2.2%
931.664917 4
 
2.2%
61.20999908 4
 
2.2%
164.2850037 4
 
2.2%
Other values (75) 123
68.3%
(Missing) 5
 
2.8%
ValueCountFrequency (%)
2.25999999 1
0.6%
2.380000114 2
1.1%
3.585099936 1
0.6%
9.364999771 1
0.6%
11.77499962 1
0.6%
12.84500027 2
1.1%
13.07470036 1
0.6%
16.39500046 2
1.1%
20.10000038 1
0.6%
23.13500023 2
1.1%
ValueCountFrequency (%)
1288.25 3
1.7%
988.0900269 1
 
0.6%
931.664917 4
2.2%
915.5100098 2
 
1.1%
800.7999878 3
1.7%
794.460022 1
 
0.6%
793.2050171 2
 
1.1%
782.625 6
3.3%
780.6528931 1
 
0.6%
767.0100098 1
 
0.6%

actual_day3_close
Real number (ℝ)

High correlation  Missing 

Distinct66
Distinct (%)37.7%
Missing5
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean276.61802
Minimum2.2850001
Maximum1288.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-07-13T20:15:06.990165image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum2.2850001
5-th percentile14.929
Q179.550003
median138.45
Q3319.72
95-th percentile923.29999
Maximum1288.25
Range1285.965
Interquartile range (IQR)240.17

Descriptive statistics

Standard deviation295.23477
Coefficient of variation (CV)1.0673013
Kurtosis1.478732
Mean276.61802
Median Absolute Deviation (MAD)82.499996
Skewness1.5186439
Sum48408.153
Variance87163.571
MonotonicityNot monotonic
2025-07-13T20:15:07.130819image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
769.6900024 8
 
4.4%
79.55000305 7
 
3.9%
130.1999969 7
 
3.9%
308.4100037 6
 
3.3%
799.4500122 6
 
3.3%
923.2999878 6
 
3.3%
72.30000305 6
 
3.3%
131.2100067 5
 
2.8%
540.5 5
 
2.8%
211.1100006 5
 
2.8%
Other values (56) 114
63.3%
ValueCountFrequency (%)
2.285000086 3
1.7%
3.494999886 1
 
0.6%
9.125 1
 
0.6%
11.52999973 1
 
0.6%
12.61499977 2
1.1%
13.19999981 1
 
0.6%
15.67000008 2
1.1%
20.22500038 1
 
0.6%
23.45999908 2
1.1%
24.28000069 1
 
0.6%
ValueCountFrequency (%)
1288.25 3
 
1.7%
982.0800171 1
 
0.6%
923.2999878 6
3.3%
799.4500122 6
3.3%
769.6900024 8
4.4%
732.7999878 4
2.2%
722.25 1
 
0.6%
560.3900146 1
 
0.6%
540.5 5
2.8%
513.5200195 2
 
1.1%

day3_min_error
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct148
Distinct (%)84.6%
Missing5
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean5.8992404
Minimum0
Maximum67.245538
Zeros2
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-07-13T20:15:07.263437image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0026793677
Q11.8286263
median3.8993411
Q36.7605652
95-th percentile16.401753
Maximum67.245538
Range67.245538
Interquartile range (IQR)4.9319389

Descriptive statistics

Standard deviation9.1307012
Coefficient of variation (CV)1.5477757
Kurtosis26.752693
Mean5.8992404
Median Absolute Deviation (MAD)2.4513177
Skewness4.7386825
Sum1032.3671
Variance83.369704
MonotonicityNot monotonic
2025-07-13T20:15:07.403537image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.28140362 3
 
1.7%
6.34581797 2
 
1.1%
4.089223267 2
 
1.1%
4.817128991 2
 
1.1%
2.782653866 2
 
1.1%
0.1385339846 2
 
1.1%
6.756109248 2
 
1.1%
0.008386766569 2
 
1.1%
4.867285398 2
 
1.1%
2.406552419 2
 
1.1%
Other values (138) 154
85.6%
(Missing) 5
 
2.8%
ValueCountFrequency (%)
0 2
1.1%
1.248418021 × 10-61
0.6%
1.897859293 × 10-61
0.6%
3.045666575 × 10-61
0.6%
3.853229713 × 10-61
0.6%
0.001651574793 1
0.6%
0.002383334331 1
0.6%
0.00255778867 1
0.6%
0.002731472934 1
0.6%
0.004094406068 1
0.6%
ValueCountFrequency (%)
67.24553828 1
0.6%
62.29314848 1
0.6%
61.53947021 1
0.6%
29.12929345 1
0.6%
28.08290572 1
0.6%
26.09993252 1
0.6%
25.51951357 1
0.6%
17.64705317 2
1.1%
15.8680528 1
0.6%
15.57157357 1
0.6%

day3_act_error
Real number (ℝ)

High correlation  Missing 

Distinct149
Distinct (%)85.1%
Missing5
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean6.9560237
Minimum0.081876065
Maximum67.359586
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-07-13T20:15:07.544157image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0.081876065
5-th percentile0.61883869
Q12.6335528
median4.7862152
Q37.8276628
95-th percentile18.428152
Maximum67.359586
Range67.27771
Interquartile range (IQR)5.19411

Descriptive statistics

Standard deviation9.2189688
Coefficient of variation (CV)1.3253217
Kurtosis24.181575
Mean6.9560237
Median Absolute Deviation (MAD)2.6110451
Skewness4.4590356
Sum1217.3041
Variance84.989386
MonotonicityNot monotonic
2025-07-13T20:15:07.683394image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.46503612 3
 
1.7%
2.81329544 2
 
1.1%
5.428298441 2
 
1.1%
5.632219495 2
 
1.1%
6.34581797 2
 
1.1%
3.079820056 2
 
1.1%
1.362183205 2
 
1.1%
8.550195189 2
 
1.1%
8.38933326 2
 
1.1%
14.86917621 2
 
1.1%
Other values (139) 154
85.6%
(Missing) 5
 
2.8%
ValueCountFrequency (%)
0.08187606481 1
0.6%
0.09724110253 1
0.6%
0.3115264798 1
0.6%
0.3605806043 1
0.6%
0.3820944104 2
1.1%
0.439978678 1
0.6%
0.5257925286 1
0.6%
0.618048846 1
0.6%
0.6191772008 1
0.6%
0.6472380274 1
0.6%
ValueCountFrequency (%)
67.35958643 1
0.6%
62.61128511 1
0.6%
61.8639657 1
0.6%
29.68290807 1
0.6%
28.75536902 1
0.6%
28.64469432 1
0.6%
26.39280919 1
0.6%
22.53828861 2
1.1%
16.66666546 1
0.6%
15.74576772 1
0.6%

made_profit_act_day_3
Categorical

High correlation 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
111 
1
69 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 111
61.7%
1 69
38.3%

Length

2025-07-13T20:15:07.812982image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-13T20:15:07.910540image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0 111
61.7%
1 69
38.3%

Most occurring characters

ValueCountFrequency (%)
0 111
61.7%
1 69
38.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 180
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 111
61.7%
1 69
38.3%

Most occurring scripts

ValueCountFrequency (%)
Common 180
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 111
61.7%
1 69
38.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 180
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 111
61.7%
1 69
38.3%

made_profit_could_day_3
Categorical

High correlation 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
98 
1
82 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 98
54.4%
1 82
45.6%

Length

2025-07-13T20:15:08.017085image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-13T20:15:08.117604image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0 98
54.4%
1 82
45.6%

Most occurring characters

ValueCountFrequency (%)
0 98
54.4%
1 82
45.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 180
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98
54.4%
1 82
45.6%

Most occurring scripts

ValueCountFrequency (%)
Common 180
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 98
54.4%
1 82
45.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 180
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 98
54.4%
1 82
45.6%

Day7_Pred
Real number (ℝ)

High correlation 

Distinct143
Distinct (%)79.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean288.22933
Minimum1.95
Maximum1400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-07-13T20:15:08.228127image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1.95
5-th percentile18.8
Q185
median154.5
Q3344.865
95-th percentile886.4105
Maximum1400
Range1398.05
Interquartile range (IQR)259.865

Descriptive statistics

Standard deviation307.37862
Coefficient of variation (CV)1.0664377
Kurtosis2.2356837
Mean288.22933
Median Absolute Deviation (MAD)85.5
Skewness1.6597508
Sum51881.28
Variance94481.617
MonotonicityNot monotonic
2025-07-13T20:15:08.362641image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
320 4
 
2.2%
85 3
 
1.7%
240 3
 
1.7%
330 3
 
1.7%
150 3
 
1.7%
225 3
 
1.7%
38 3
 
1.7%
125 3
 
1.7%
128 2
 
1.1%
23.2 2
 
1.1%
Other values (133) 151
83.9%
ValueCountFrequency (%)
1.95 1
0.6%
3 1
0.6%
3.2 1
0.6%
5 1
0.6%
10 1
0.6%
10.2 1
0.6%
14 1
0.6%
15 2
1.1%
19 2
1.1%
21 1
0.6%
ValueCountFrequency (%)
1400 2
1.1%
1321.23 1
0.6%
1320 1
0.6%
1050 1
0.6%
1000 2
1.1%
940 2
1.1%
883.59 1
0.6%
883.49 1
0.6%
875.5 2
1.1%
865.5 1
0.6%

closest_day7_price
Real number (ℝ)

High correlation 

Distinct81
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean285.77946
Minimum2.3002999
Maximum1255.1899
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-07-13T20:15:08.500671image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum2.3002999
5-th percentile15.276755
Q175.839996
median144.1528
Q3322.95541
95-th percentile922.09998
Maximum1255.1899
Range1252.8896
Interquartile range (IQR)247.11542

Descriptive statistics

Standard deviation299.08087
Coefficient of variation (CV)1.0465443
Kurtosis0.89427888
Mean285.77946
Median Absolute Deviation (MAD)85.352802
Skewness1.3799284
Sum51440.303
Variance89449.365
MonotonicityNot monotonic
2025-07-13T20:15:08.645325image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
749.3939819 7
 
3.9%
128.8600006 7
 
3.9%
75.83999634 7
 
3.9%
73.15000153 6
 
3.3%
796.6400146 6
 
3.3%
305.0700073 6
 
3.3%
538.9199829 5
 
2.8%
167.5 4
 
2.2%
224.7400055 4
 
2.2%
77.01999664 4
 
2.2%
Other values (71) 124
68.9%
ValueCountFrequency (%)
2.300299883 1
0.6%
2.345000029 2
1.1%
4.040100098 1
0.6%
9.385000229 1
0.6%
11.62969971 1
0.6%
12.73999977 2
1.1%
13.16499996 1
0.6%
15.38790035 2
1.1%
21.14119911 1
0.6%
23.20479965 2
1.1%
ValueCountFrequency (%)
1255.189941 3
1.7%
977.2000122 1
 
0.6%
929.2550049 4
2.2%
922.0999756 3
1.7%
801.4400024 3
1.7%
796.6400146 6
3.3%
749.3939819 7
3.9%
743.9299927 1
 
0.6%
724.3150024 4
2.2%
713.8499756 1
 
0.6%

actual_day7_close
Real number (ℝ)

High correlation 

Distinct108
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean285.08447
Minimum2.3106
Maximum1249.575
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-07-13T20:15:08.780528image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum2.3106
5-th percentile15.20781
Q174.815002
median143.5078
Q3322.32999
95-th percentile928.995
Maximum1249.575
Range1247.2644
Interquartile range (IQR)247.51498

Descriptive statistics

Standard deviation298.52099
Coefficient of variation (CV)1.0471317
Kurtosis0.88952801
Mean285.08447
Median Absolute Deviation (MAD)85.037806
Skewness1.3807798
Sum51315.204
Variance89114.781
MonotonicityNot monotonic
2025-07-13T20:15:08.922191image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
304.1499939 6
 
3.3%
798.9667969 6
 
3.3%
928.9949951 5
 
2.8%
211.5449982 4
 
2.2%
74.31999969 4
 
2.2%
128.3099976 4
 
2.2%
748.670105 4
 
2.2%
74.81500244 4
 
2.2%
310.2309875 3
 
1.7%
716.4799805 3
 
1.7%
Other values (98) 137
76.1%
ValueCountFrequency (%)
2.310600042 3
1.7%
3.98239994 1
 
0.6%
9.164999962 1
 
0.6%
11.54500008 1
 
0.6%
12.72500038 1
 
0.6%
12.72929955 1
 
0.6%
13.36499977 1
 
0.6%
15.30480003 2
1.1%
21.69499969 1
 
0.6%
23.42110062 1
 
0.6%
ValueCountFrequency (%)
1249.574951 1
 
0.6%
1249.070557 2
 
1.1%
969.4299927 1
 
0.6%
929.1450195 2
 
1.1%
928.9949951 5
2.8%
799.2000122 1
 
0.6%
798.9667969 6
3.3%
798.9500122 2
 
1.1%
748.75 1
 
0.6%
748.670105 4
2.2%

day7_min_error
Real number (ℝ)

High correlation 

Distinct156
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.288718
Minimum0
Maximum636.6139
Zeros1
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-07-13T20:15:09.056783image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.64792322
Q14.1699222
median7.378127
Q313.867228
95-th percentile29.061841
Maximum636.6139
Range636.6139
Interquartile range (IQR)9.6973054

Descriptive statistics

Standard deviation48.434587
Coefficient of variation (CV)3.3897085
Kurtosis154.48937
Mean14.288718
Median Absolute Deviation (MAD)4.1081175
Skewness12.039112
Sum2571.9692
Variance2345.9093
MonotonicityNot monotonic
2025-07-13T20:15:09.195924image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.893956245 4
 
2.2%
7.77085028 3
 
1.7%
6.790065913 2
 
1.1%
0.02068387649 2
 
1.1%
10.75949902 2
 
1.1%
5.898211017 2
 
1.1%
1.156302098 2
 
1.1%
6.868977654 2
 
1.1%
5.263155699 2
 
1.1%
7.613087338 2
 
1.1%
Other values (146) 157
87.2%
ValueCountFrequency (%)
0 1
0.6%
0.004953946145 1
0.6%
0.006517761517 1
0.6%
0.01010466783 1
0.6%
0.01176860393 1
0.6%
0.02068387649 2
1.1%
0.1611016955 1
0.6%
0.2686300724 1
0.6%
0.6678860134 1
0.6%
1.156302098 2
1.1%
ValueCountFrequency (%)
636.6138986 1
0.6%
93.64832307 1
0.6%
84.29654578 1
0.6%
65.2507625 1
0.6%
62.05509063 1
0.6%
60.69298237 1
0.6%
43.28489697 1
0.6%
36.46055271 1
0.6%
30.61465271 1
0.6%
28.98011452 1
0.6%

day7_act_error
Real number (ℝ)

High correlation 

Distinct166
Distinct (%)92.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.864559
Minimum0.049428248
Maximum643.07795
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.8 KiB
2025-07-13T20:15:09.373449image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0.049428248
5-th percentile1.3084229
Q14.4792625
median7.7315999
Q314.063129
95-th percentile30.008862
Maximum643.07795
Range643.02852
Interquartile range (IQR)9.5838661

Descriptive statistics

Standard deviation48.855203
Coefficient of variation (CV)3.2866905
Kurtosis154.9701
Mean14.864559
Median Absolute Deviation (MAD)4.3291188
Skewness12.064081
Sum2675.6206
Variance2386.8309
MonotonicityNot monotonic
2025-07-13T20:15:10.066577image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.211246563 4
 
2.2%
7.643206396 2
 
1.1%
6.360349783 2
 
1.1%
2.584862109 2
 
1.1%
6.372352616 2
 
1.1%
9.13626546 2
 
1.1%
7.024220858 2
 
1.1%
9.090228646 2
 
1.1%
24.14405911 2
 
1.1%
15.49372833 2
 
1.1%
Other values (156) 158
87.8%
ValueCountFrequency (%)
0.04942824829 1
0.6%
0.1186032318 1
0.6%
0.176161507 1
0.6%
0.3453364375 1
0.6%
0.5590716526 1
0.6%
0.9440231699 1
0.6%
1.044152346 1
0.6%
1.168276237 1
0.6%
1.184614012 1
0.6%
1.314939129 1
0.6%
ValueCountFrequency (%)
643.0779454 1
0.6%
93.66682067 1
0.6%
84.34227799 1
0.6%
65.470773 1
0.6%
62.33671851 1
0.6%
60.99101944 1
0.6%
42.22143359 1
0.6%
38.492164 1
0.6%
31.57612915 1
0.6%
29.9263742 1
0.6%

made_profit_could_day_7
Categorical

High correlation 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
112 
1
68 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 112
62.2%
1 68
37.8%

Length

2025-07-13T20:15:10.196612image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-13T20:15:10.295173image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0 112
62.2%
1 68
37.8%

Most occurring characters

ValueCountFrequency (%)
0 112
62.2%
1 68
37.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 180
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 112
62.2%
1 68
37.8%

Most occurring scripts

ValueCountFrequency (%)
Common 180
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 112
62.2%
1 68
37.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 180
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 112
62.2%
1 68
37.8%

made_profit_act_day_7
Categorical

High correlation 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
118 
1
62 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 118
65.6%
1 62
34.4%

Length

2025-07-13T20:15:10.402730image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-13T20:15:10.502222image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0 118
65.6%
1 62
34.4%

Most occurring characters

ValueCountFrequency (%)
0 118
65.6%
1 62
34.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 180
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 118
65.6%
1 62
34.4%

Most occurring scripts

ValueCountFrequency (%)
Common 180
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 118
65.6%
1 62
34.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 180
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 118
65.6%
1 62
34.4%

Query
Text

Distinct75
Distinct (%)41.7%
Missing0
Missing (%)0.0%
Memory size47.7 KiB
2025-07-13T20:15:10.718958image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length160
Median length125
Mean length113.97222
Min length68

Characters and Unicode

Total characters20515
Distinct characters83
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)10.6%

Sample

1st row1. I bought Nvidia (NVDA) at $960 but it’s now near $1,200. Should I trim my position or continue riding the AI wave?
2nd row2. With Amazon (AMZN) up after its Prime Day preview, is it smart to buy now or wait for a post‑event dip?
3rd row3. Tesla (TSLA) just announced another price cut in Europe. Does that make the stock a buy, sell, or hold for the next 12 months?
4th row4. After the recent pullback in Advanced Micro Devices (AMD), is this an attractive entry point or a value trap?
5th row5. Is Meta Platforms (META) still a good buy given its metaverse spending, or should I rotate into Alphabet (GOOGL)?
ValueCountFrequency (%)
and 157
 
4.4%
i 122
 
3.4%
or 120
 
3.4%
should 87
 
2.5%
is 59
 
1.7%
a 57
 
1.6%
the 56
 
1.6%
to 55
 
1.5%
for 52
 
1.5%
in 52
 
1.5%
Other values (575) 2733
77.0%
2025-07-13T20:15:11.096235image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3369
 
16.4%
o 1317
 
6.4%
e 1242
 
6.1%
t 1091
 
5.3%
a 926
 
4.5%
i 904
 
4.4%
r 881
 
4.3%
n 833
 
4.1%
s 735
 
3.6%
d 637
 
3.1%
Other values (73) 8580
41.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12365
60.3%
Space Separator 3370
 
16.4%
Uppercase Letter 2735
 
13.3%
Other Punctuation 716
 
3.5%
Decimal Number 451
 
2.2%
Close Punctuation 275
 
1.3%
Open Punctuation 275
 
1.3%
Control 190
 
0.9%
Dash Punctuation 79
 
0.4%
Final Punctuation 47
 
0.2%
Other values (2) 12
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1317
10.7%
e 1242
 
10.0%
t 1091
 
8.8%
a 926
 
7.5%
i 904
 
7.3%
r 881
 
7.1%
n 833
 
6.7%
s 735
 
5.9%
d 637
 
5.2%
l 588
 
4.8%
Other values (16) 3211
26.0%
Uppercase Letter
ValueCountFrequency (%)
I 320
 
11.7%
A 265
 
9.7%
S 257
 
9.4%
M 176
 
6.4%
L 166
 
6.1%
N 165
 
6.0%
C 160
 
5.9%
T 144
 
5.3%
D 135
 
4.9%
P 125
 
4.6%
Other values (16) 822
30.1%
Decimal Number
ValueCountFrequency (%)
2 74
16.4%
4 58
12.9%
1 57
12.6%
3 49
10.9%
6 49
10.9%
0 39
8.6%
5 34
7.5%
8 33
7.3%
7 32
7.1%
9 26
 
5.8%
Other Punctuation
ValueCountFrequency (%)
. 268
37.4%
, 254
35.5%
? 183
25.6%
% 6
 
0.8%
' 3
 
0.4%
; 2
 
0.3%
Dash Punctuation
ValueCountFrequency (%)
- 55
69.6%
13
 
16.5%
8
 
10.1%
3
 
3.8%
Space Separator
ValueCountFrequency (%)
3369
> 99.9%
1
 
< 0.1%
Control
ValueCountFrequency (%)
95
50.0%
95
50.0%
Math Symbol
ValueCountFrequency (%)
+ 4
57.1%
~ 3
42.9%
Currency Symbol
ValueCountFrequency (%)
3
60.0%
$ 2
40.0%
Close Punctuation
ValueCountFrequency (%)
) 275
100.0%
Open Punctuation
ValueCountFrequency (%)
( 275
100.0%
Final Punctuation
ValueCountFrequency (%)
47
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15100
73.6%
Common 5415
 
26.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 1317
 
8.7%
e 1242
 
8.2%
t 1091
 
7.2%
a 926
 
6.1%
i 904
 
6.0%
r 881
 
5.8%
n 833
 
5.5%
s 735
 
4.9%
d 637
 
4.2%
l 588
 
3.9%
Other values (42) 5946
39.4%
Common
ValueCountFrequency (%)
3369
62.2%
) 275
 
5.1%
( 275
 
5.1%
. 268
 
4.9%
, 254
 
4.7%
? 183
 
3.4%
95
 
1.8%
95
 
1.8%
2 74
 
1.4%
4 58
 
1.1%
Other values (21) 469
 
8.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20440
99.6%
Punctuation 72
 
0.4%
Currency Symbols 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3369
 
16.5%
o 1317
 
6.4%
e 1242
 
6.1%
t 1091
 
5.3%
a 926
 
4.5%
i 904
 
4.4%
r 881
 
4.3%
n 833
 
4.1%
s 735
 
3.6%
d 637
 
3.1%
Other values (67) 8505
41.6%
Punctuation
ValueCountFrequency (%)
47
65.3%
13
 
18.1%
8
 
11.1%
3
 
4.2%
1
 
1.4%
Currency Symbols
ValueCountFrequency (%)
3
100.0%

Advice
Text

Distinct75
Distinct (%)41.7%
Missing0
Missing (%)0.0%
Memory size68.1 KiB
2025-07-13T20:15:11.324324image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Length

Max length761
Median length349
Mean length315.04444
Min length4

Characters and Unicode

Total characters56708
Distinct characters78
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)10.6%

Sample

1st rowContinue riding the AI wave, but consider trimming your position by 10-20% to lock in some profits.
2nd rowBuy now, as the stock is likely to continue its upward trend after the Prime Day preview. However, keep an eye on the market news and adjust your strategy accordingly.
3rd rowHold
4th rowBuy, as the recent pullback in AMD presents an attractive entry point due to its strong revenue growth in the data center segment and Cathie Wood's confidence in its growth prospects.
5th row{'stock': 'META', 'reason': 'Meta Platforms, Inc. is still a somewhat bullish stock due to its growing metaverse spending, which is seen as a promising area for future growth.', 'rotation': 'No, do not rotate into Alphabet (GOOGL) as it is a somewhat bearish stock due to the decline in search queries and the increasing competition from AI chatbots.'}
ValueCountFrequency (%)
and 440
 
5.0%
the 380
 
4.3%
to 355
 
4.0%
a 322
 
3.6%
portfolio 140
 
1.6%
is 132
 
1.5%
your 128
 
1.4%
on 120
 
1.4%
growth 116
 
1.3%
for 113
 
1.3%
Other values (716) 6633
74.7%
2025-07-13T20:15:11.718414image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8699
15.3%
e 4654
 
8.2%
t 3922
 
6.9%
o 3865
 
6.8%
n 3254
 
5.7%
i 3202
 
5.6%
a 3179
 
5.6%
s 2877
 
5.1%
r 2708
 
4.8%
d 1724
 
3.0%
Other values (68) 18624
32.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 41121
72.5%
Space Separator 8699
 
15.3%
Uppercase Letter 3358
 
5.9%
Other Punctuation 2544
 
4.5%
Decimal Number 310
 
0.5%
Close Punctuation 238
 
0.4%
Open Punctuation 238
 
0.4%
Dash Punctuation 135
 
0.2%
Connector Punctuation 61
 
0.1%
Math Symbol 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 4654
11.3%
t 3922
 
9.5%
o 3865
 
9.4%
n 3254
 
7.9%
i 3202
 
7.8%
a 3179
 
7.7%
s 2877
 
7.0%
r 2708
 
6.6%
d 1724
 
4.2%
l 1694
 
4.1%
Other values (16) 10042
24.4%
Uppercase Letter
ValueCountFrequency (%)
A 290
 
8.6%
I 272
 
8.1%
S 272
 
8.1%
T 238
 
7.1%
N 224
 
6.7%
B 219
 
6.5%
C 215
 
6.4%
M 183
 
5.4%
H 177
 
5.3%
L 170
 
5.1%
Other values (16) 1098
32.7%
Decimal Number
ValueCountFrequency (%)
0 93
30.0%
2 65
21.0%
3 57
18.4%
5 30
 
9.7%
1 27
 
8.7%
4 21
 
6.8%
6 7
 
2.3%
7 6
 
1.9%
8 4
 
1.3%
Other Punctuation
ValueCountFrequency (%)
' 984
38.7%
, 684
26.9%
. 503
19.8%
: 254
 
10.0%
" 64
 
2.5%
% 55
 
2.2%
Close Punctuation
ValueCountFrequency (%)
) 130
54.6%
} 93
39.1%
] 15
 
6.3%
Open Punctuation
ValueCountFrequency (%)
( 130
54.6%
{ 93
39.1%
[ 15
 
6.3%
Dash Punctuation
ValueCountFrequency (%)
- 132
97.8%
3
 
2.2%
Space Separator
ValueCountFrequency (%)
8699
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 61
100.0%
Math Symbol
ValueCountFrequency (%)
+ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 44479
78.4%
Common 12229
 
21.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 4654
 
10.5%
t 3922
 
8.8%
o 3865
 
8.7%
n 3254
 
7.3%
i 3202
 
7.2%
a 3179
 
7.1%
s 2877
 
6.5%
r 2708
 
6.1%
d 1724
 
3.9%
l 1694
 
3.8%
Other values (42) 13400
30.1%
Common
ValueCountFrequency (%)
8699
71.1%
' 984
 
8.0%
, 684
 
5.6%
. 503
 
4.1%
: 254
 
2.1%
- 132
 
1.1%
) 130
 
1.1%
( 130
 
1.1%
0 93
 
0.8%
} 93
 
0.8%
Other values (16) 527
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 56705
> 99.9%
Punctuation 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8699
15.3%
e 4654
 
8.2%
t 3922
 
6.9%
o 3865
 
6.8%
n 3254
 
5.7%
i 3202
 
5.6%
a 3179
 
5.6%
s 2877
 
5.1%
r 2708
 
4.8%
d 1724
 
3.0%
Other values (67) 18621
32.8%
Punctuation
ValueCountFrequency (%)
3
100.0%

profit_1
Boolean

High correlation 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
False
94 
True
86 
ValueCountFrequency (%)
False 94
52.2%
True 86
47.8%
2025-07-13T20:15:11.825927image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

profit_3
Boolean

High correlation 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
False
99 
True
81 
ValueCountFrequency (%)
False 99
55.0%
True 81
45.0%
2025-07-13T20:15:11.923474image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

profit_7
Boolean

High correlation 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
False
106 
True
74 
ValueCountFrequency (%)
False 106
58.9%
True 74
41.1%
2025-07-13T20:15:12.019032image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Category
Categorical

Distinct13
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Memory size13.3 KiB
Technology
57 
Semiconductors
26 
Consumer
25 
Healthcare
11 
Media Entertainment
11 
Other values (8)
50 

Length

Max length19
Median length14
Mean length10.744444
Min length7

Characters and Unicode

Total characters1934
Distinct characters31
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st rowTechnology
2nd rowConsumer
3rd rowEV Auto
4th rowTechnology
5th rowTechnology

Common Values

ValueCountFrequency (%)
Technology 57
31.7%
Semiconductors 26
14.4%
Consumer 25
13.9%
Healthcare 11
 
6.1%
Media Entertainment 11
 
6.1%
Real Estate 10
 
5.6%
Cloud Data 10
 
5.6%
FinTech 9
 
5.0%
EV Auto 9
 
5.0%
Cybersecurity 4
 
2.2%
Other values (3) 8
 
4.4%

Length

2025-07-13T20:15:12.128663image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
technology 57
25.4%
semiconductors 26
11.6%
consumer 25
11.2%
healthcare 11
 
4.9%
media 11
 
4.9%
entertainment 11
 
4.9%
real 10
 
4.5%
estate 10
 
4.5%
cloud 10
 
4.5%
data 10
 
4.5%
Other values (8) 43
19.2%

Most occurring characters

ValueCountFrequency (%)
o 224
 
11.6%
e 209
 
10.8%
n 158
 
8.2%
c 137
 
7.1%
t 120
 
6.2%
l 88
 
4.6%
a 88
 
4.6%
r 85
 
4.4%
h 77
 
4.0%
u 77
 
4.0%
Other values (21) 671
34.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1648
85.2%
Uppercase Letter 242
 
12.5%
Space Separator 44
 
2.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 224
13.6%
e 209
12.7%
n 158
9.6%
c 137
 
8.3%
t 120
 
7.3%
l 88
 
5.3%
a 88
 
5.3%
r 85
 
5.2%
h 77
 
4.7%
u 77
 
4.7%
Other values (8) 385
23.4%
Uppercase Letter
ValueCountFrequency (%)
T 66
27.3%
C 42
17.4%
E 30
12.4%
S 26
 
10.7%
R 14
 
5.8%
H 11
 
4.5%
M 11
 
4.5%
D 10
 
4.1%
F 10
 
4.1%
V 9
 
3.7%
Other values (2) 13
 
5.4%
Space Separator
ValueCountFrequency (%)
44
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1890
97.7%
Common 44
 
2.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 224
 
11.9%
e 209
 
11.1%
n 158
 
8.4%
c 137
 
7.2%
t 120
 
6.3%
l 88
 
4.7%
a 88
 
4.7%
r 85
 
4.5%
h 77
 
4.1%
u 77
 
4.1%
Other values (20) 627
33.2%
Common
ValueCountFrequency (%)
44
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1934
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 224
 
11.6%
e 209
 
10.8%
n 158
 
8.2%
c 137
 
7.1%
t 120
 
6.2%
l 88
 
4.6%
a 88
 
4.6%
r 85
 
4.4%
h 77
 
4.0%
u 77
 
4.0%
Other values (21) 671
34.7%
Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
140 
1
40 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 140
77.8%
1 40
 
22.2%

Length

2025-07-13T20:15:12.238157image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-13T20:15:12.338679image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0 140
77.8%
1 40
 
22.2%

Most occurring characters

ValueCountFrequency (%)
0 140
77.8%
1 40
 
22.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 180
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 140
77.8%
1 40
 
22.2%

Most occurring scripts

ValueCountFrequency (%)
Common 180
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 140
77.8%
1 40
 
22.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 180
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 140
77.8%
1 40
 
22.2%
Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
160 
1
20 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 160
88.9%
1 20
 
11.1%

Length

2025-07-13T20:15:12.443305image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-13T20:15:12.542796image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0 160
88.9%
1 20
 
11.1%

Most occurring characters

ValueCountFrequency (%)
0 160
88.9%
1 20
 
11.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 180
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 160
88.9%
1 20
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Common 180
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 160
88.9%
1 20
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 180
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 160
88.9%
1 20
 
11.1%

missed_opportunity_day7
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.6 KiB
0
170 
1
 
10

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters180
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 170
94.4%
1 10
 
5.6%

Length

2025-07-13T20:15:12.647414image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-13T20:15:12.745968image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0 170
94.4%
1 10
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0 170
94.4%
1 10
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 180
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 170
94.4%
1 10
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common 180
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 170
94.4%
1 10
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 180
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 170
94.4%
1 10
 
5.6%

Date
Date

Constant 

Distinct1
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
Minimum2025-07-03 00:00:00
Maximum2025-07-03 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-13T20:15:12.829491image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:15:12.922047image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2025-07-13T20:15:00.124330image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:34.163095image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:36.624439image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:38.149884image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:39.667694image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:41.148690image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:42.675324image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:44.178678image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:46.189925image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:47.683397image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:49.162530image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:50.638405image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:52.201763image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:53.760469image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:55.257442image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:57.165834image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:58.637748image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:15:00.213848image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:34.261650image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:36.715026image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:38.248411image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:39.756671image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:41.238304image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:42.766838image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:44.268262image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:46.279579image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:47.774892image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:49.253112image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:50.725846image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:52.298287image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:53.851957image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:55.346954image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:57.253395image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:58.726399image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:15:00.297371image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:34.361186image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:36.833553image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:38.337645image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:39.841185image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:41.332857image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:42.853910image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:44.353338image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:46.366066image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:47.858964image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:49.336693image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:50.814404image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:52.388357image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:53.936511image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:55.432512image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:57.336990image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:58.809082image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:15:00.387968image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:34.461802image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:36.933155image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:38.427196image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:39.928770image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:41.440919image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:42.944966image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:44.443919image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:46.454143image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:47.949520image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:49.426258image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:50.907962image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:52.482442image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:54.026115image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:55.518992image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:57.423541image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:58.898611image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:15:00.473011image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:34.616293image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:37.021734image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
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2025-07-13T20:15:01.559090image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:36.531889image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:38.065805image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:39.582076image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:41.062687image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:42.569734image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:44.094021image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:46.104365image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:47.596874image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:49.076528image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:50.553823image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:52.106243image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:53.677429image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:55.171441image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:57.081793image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:14:58.553194image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-07-13T20:15:00.036216image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Correlations

2025-07-13T20:15:13.033576image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
BuyCategoryCurr_PriceDay1_PredDay3_PredDay7_PredHoldSellUnnamed: 0actual_day1_closeactual_day3_closeactual_day7_closeclosest_day1_priceclosest_day3_priceclosest_day7_priceday1_act_errorday1_min_errorday3_act_errorday3_min_errorday7_act_errorday7_min_errormade_profit_act_day_1made_profit_act_day_3made_profit_act_day_7made_profit_could_day_1made_profit_could_day_3made_profit_could_day_7missed_opportunity_day1missed_opportunity_day3missed_opportunity_day7profit_1profit_3profit_7
Buy1.0000.0760.0000.0000.0000.0000.4390.3510.1270.0310.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0580.0000.0000.0000.0000.0000.0000.0610.0000.1880.0900.000
Category0.0761.0000.4600.3700.3780.3550.2010.0000.1640.4640.4650.4540.4640.4560.4540.2230.2140.1590.1190.0000.0000.2890.0870.1340.2390.1820.1650.2880.3580.1010.1980.0000.000
Curr_Price0.0000.4601.0000.9110.9100.9070.0000.191-0.0440.9990.9980.8820.9990.9990.884-0.0640.098-0.128-0.088-0.153-0.1540.2100.0000.0910.1690.0000.1740.2950.2280.5410.0750.0000.000
Day1_Pred0.0000.3700.9111.0001.0000.9980.0000.235-0.0320.9920.9920.9490.9950.9940.952-0.0710.094-0.137-0.095-0.070-0.0700.2620.0330.0000.0480.1310.1860.1970.3300.4190.1970.0000.000
Day3_Pred0.0000.3780.9101.0001.0000.9990.0000.235-0.0330.9920.9910.9480.9940.9930.951-0.0700.097-0.134-0.092-0.065-0.0650.2480.1300.0000.0000.1240.2090.1970.3570.3810.1840.0000.000
Day7_Pred0.0000.3550.9070.9980.9991.0000.0000.211-0.0370.9900.9900.9470.9920.9920.950-0.0610.104-0.124-0.082-0.054-0.0540.2250.1310.0000.0530.1180.2030.1680.3550.3810.1550.0000.000
Hold0.4390.2010.0000.0000.0000.0001.0000.3770.0850.0720.0000.0000.0000.0000.0000.1690.1950.1380.1850.0000.0000.1990.0580.0000.1030.1650.0000.2870.0000.0000.3170.0000.051
Sell0.3510.0000.1910.2350.2350.2110.3771.0000.1360.1870.2110.1820.1860.1860.1820.0000.0000.0870.1150.0000.0000.5830.1300.1860.0400.0000.1680.2470.0640.0000.4740.0000.089
Unnamed: 00.1270.164-0.044-0.032-0.033-0.0370.0850.1361.000-0.010-0.0100.007-0.014-0.0130.003-0.050-0.065-0.005-0.0100.0740.0830.0720.0000.0000.0000.0500.0670.1860.0680.1360.1730.0000.000
actual_day1_close0.0310.4640.9990.9920.9920.9900.0720.187-0.0101.0000.9980.9970.9990.9980.997-0.0770.089-0.135-0.090-0.084-0.0840.2100.0000.1060.0990.0280.1370.3130.2080.5210.1050.0000.000
actual_day3_close0.0000.4650.9980.9920.9910.9900.0000.211-0.0100.9981.0000.9990.9981.0000.999-0.0740.090-0.142-0.102-0.092-0.0910.2480.0080.2740.1350.0000.2390.2850.2480.2980.1320.0000.107
actual_day7_close0.0000.4540.8820.9490.9480.9470.0000.1820.0070.9970.9991.0000.9960.9980.999-0.0670.096-0.138-0.099-0.049-0.0480.3300.0000.2250.2410.0000.1680.3210.2220.2880.2250.0000.000
closest_day1_price0.0000.4640.9990.9950.9940.9920.0000.186-0.0140.9990.9980.9961.0000.9990.997-0.0740.091-0.134-0.090-0.083-0.0830.1780.0000.1050.1330.0000.1590.2930.2110.5270.0000.0000.000
closest_day3_price0.0000.4560.9990.9940.9930.9920.0000.186-0.0130.9981.0000.9980.9991.0000.999-0.0700.093-0.139-0.098-0.088-0.0880.1980.0000.1200.0860.0000.1260.2930.2380.4870.0620.0000.000
closest_day7_price0.0000.4540.8840.9520.9510.9500.0000.1820.0030.9970.9990.9990.9970.9991.000-0.0680.096-0.139-0.099-0.051-0.0500.3300.0000.2250.2410.0000.1680.3210.2220.2880.2250.0000.000
day1_act_error0.0000.223-0.064-0.071-0.070-0.0610.1690.000-0.050-0.077-0.074-0.067-0.074-0.070-0.0681.0000.9020.7700.7640.6350.6390.1240.0640.0560.2400.0850.0740.0000.0000.0000.0000.1100.037
day1_min_error0.0000.2140.0980.0940.0970.1040.1950.000-0.0650.0890.0900.0960.0910.0930.0960.9021.0000.7380.7480.6440.6450.1160.1400.1230.2590.1640.1390.0000.0000.0000.0000.1140.082
day3_act_error0.0000.159-0.128-0.137-0.134-0.1240.1380.087-0.005-0.135-0.142-0.138-0.134-0.139-0.1390.7700.7381.0000.9710.8740.8770.1570.1460.1320.2010.1180.2180.0590.0000.0000.0630.0000.000
day3_min_error0.0000.119-0.088-0.095-0.092-0.0820.1850.115-0.010-0.090-0.102-0.099-0.090-0.098-0.0990.7640.7480.9711.0000.8750.8770.1740.1840.1530.2680.1680.2010.0710.0000.0000.2100.1420.083
day7_act_error0.0000.000-0.153-0.070-0.065-0.0540.0000.0000.074-0.084-0.092-0.049-0.083-0.088-0.0510.6350.6440.8740.8751.0000.9960.0000.0550.0280.1200.0890.0510.0000.0000.0000.0000.0860.069
day7_min_error0.0000.000-0.154-0.070-0.065-0.0540.0000.0000.083-0.084-0.091-0.048-0.083-0.088-0.0500.6390.6450.8770.8770.9961.0000.0000.0550.0280.1200.0890.0510.0000.0000.0000.0000.0860.069
made_profit_act_day_10.0580.2890.2100.2620.2480.2250.1990.5830.0720.2100.2480.3300.1780.1980.3300.1240.1160.1570.1740.0000.0001.0000.1790.1240.3820.2200.2150.4120.0000.1600.8310.0320.000
made_profit_act_day_30.0000.0870.0000.0330.1300.1310.0580.1300.0000.0000.0080.0000.0000.0000.0000.0640.1400.1460.1840.0550.0550.1791.0000.5920.1700.6880.6200.0000.2500.0370.0000.8590.463
made_profit_act_day_70.0000.1340.0910.0000.0000.0000.0000.1860.0000.1060.2740.2250.1050.1200.2250.0560.1230.1320.1530.0280.0280.1240.5921.0000.0000.5190.8210.0540.0000.1310.0000.4800.855
made_profit_could_day_10.0000.2390.1690.0480.0000.0530.1030.0400.0000.0990.1350.2410.1330.0860.2410.2400.2590.2010.2680.1200.1200.3820.1700.0001.0000.2870.1050.4880.0730.1920.1890.0000.128
made_profit_could_day_30.0000.1820.0000.1310.1240.1180.1650.0000.0500.0280.0000.0000.0000.0000.0000.0850.1640.1180.1680.0890.0890.2200.6880.5190.2871.0000.6070.0000.3620.1230.0000.5480.374
made_profit_could_day_70.0000.1650.1740.1860.2090.2030.0000.1680.0670.1370.2390.1680.1590.1260.1680.0740.1390.2180.2010.0510.0510.2150.6200.8210.1050.6071.0000.0000.0000.2770.0540.5000.686
missed_opportunity_day10.0000.2880.2950.1970.1970.1680.2870.2470.1860.3130.2850.3210.2930.2930.3210.0000.0000.0590.0710.0000.0000.4120.0000.0540.4880.0000.0001.0000.0000.0000.4940.0000.144
missed_opportunity_day30.0610.3580.2280.3300.3570.3550.0000.0640.0680.2080.2480.2220.2110.2380.2220.0000.0000.0000.0000.0000.0000.0000.2500.0000.0730.3620.0000.0001.0000.0000.0000.2930.000
missed_opportunity_day70.0000.1010.5410.4190.3810.3810.0000.0000.1360.5210.2980.2880.5270.4870.2880.0000.0000.0000.0000.0000.0000.1600.0370.1310.1920.1230.2770.0000.0001.0000.1090.0000.162
profit_10.1880.1980.0750.1970.1840.1550.3170.4740.1730.1050.1320.2250.0000.0620.2250.0000.0000.0630.2100.0000.0000.8310.0000.0000.1890.0000.0540.4940.0000.1091.0000.1330.057
profit_30.0900.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.1100.1140.0000.1420.0860.0860.0320.8590.4800.0000.5480.5000.0000.2930.0000.1331.0000.592
profit_70.0000.0000.0000.0000.0000.0000.0510.0890.0000.0000.1070.0000.0000.0000.0000.0370.0820.0000.0830.0690.0690.0000.4630.8550.1280.3740.6860.1440.0000.1620.0570.5921.000

Missing values

2025-07-13T20:15:01.738142image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-13T20:15:02.209072image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-07-13T20:15:02.469187image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0Date TimeStockCurr_PriceBuySellHoldDay1_Predclosest_day1_priceactual_day1_closeday1_min_errorday1_act_errormade_profit_could_day_1made_profit_act_day_1Day3_Predclosest_day3_priceactual_day3_closeday3_min_errorday3_act_errormade_profit_act_day_3made_profit_could_day_3Day7_Predclosest_day7_priceactual_day7_closeday7_min_errorday7_act_errormade_profit_could_day_7made_profit_act_day_7QueryAdviceprofit_1profit_3profit_7Categorymissed_opportunity_day1missed_opportunity_day3missed_opportunity_day7Date
002025-07-03 00:00NVDA159.271103010162.50159.160004158.2400052.0985152.69211011165.80164.285004162.8300020.9221761.82398700170.20167.500000167.3598941.6119401.697005001. I bought Nvidia (NVDA) at $960 but it’s now near $1,200. Should I trim my position or continue riding the AI wave?\r\nContinue riding the AI wave, but consider trimming your position by 10-20% to lock in some profits.TrueFalseFalseTechnology0002025-07-03
112025-07-03 00:00AMZN223.289993100230.00224.220001223.4799962.5778252.91748911235.00224.199997222.4700014.8171295.63221901240.00224.740005224.3755956.7900666.963505112. With Amazon (AMZN) up after its Prime Day preview, is it smart to buy now or wait for a post‑event dip?\r\nBuy now, as the stock is likely to continue its upward trend after the Prime Day preview. However, keep an eye on the market news and adjust your strategy accordingly.TrueFalseTrueConsumer0102025-07-03
222025-07-03 00:00TSLA315.184998001310.23295.929993293.9200134.8322265.54912400308.56299.359985295.8200073.0732284.30667000306.89306.910004310.9791870.0065181.314939003. Tesla (TSLA) just announced another price cut in Europe. Does that make the stock a buy, sell, or hold for the next 12 months?\r\nHoldFalseFalseFalseEV Auto0002025-07-03
332025-07-03 00:00AMD137.949997100145.00137.175003134.8300025.7043907.54283000150.00140.479095138.4499976.7774538.34236411155.00144.070007144.0473027.5865847.603542114. After the recent pullback in Advanced Micro Devices (AMD), is this an attractive entry point or a value trap?\r\nBuy, as the recent pullback in AMD presents an attractive entry point due to its strong revenue growth in the data center segment and Cathie Wood's confidence in its growth prospects.FalseTrueTrueTechnology0002025-07-03
442025-07-03 00:00META718.599976001732.00726.505005718.6400150.7563601.85906511745.00737.109985732.7999881.0703991.66484911760.00724.315002716.6199954.9267246.053418105. Is Meta Platforms (META) still a good buy given its metaverse spending, or should I rotate into Alphabet (GOOGL)?\r\n{'stock': 'META', 'reason': 'Meta Platforms, Inc. is still a somewhat bullish stock due to its growing metaverse spending, which is seen as a promising area for future growth.', 'rotation': 'No, do not rotate into Alphabet (GOOGL) as it is a somewhat bearish stock due to the decline in search queries and the increasing competition from AI chatbots.'}TrueTrueFalseTechnology0012025-07-03
552025-07-03 00:00GOOGL179.490005010182.00179.029999176.7899931.6589412.94700311190.00179.320007176.6950075.9558297.52992011200.00177.787796177.44500712.49366112.710976115. Is Meta Platforms (META) still a good buy given its metaverse spending, or should I rotate into Alphabet (GOOGL)?\r\n{'stock': 'META', 'reason': 'Meta Platforms, Inc. is still a somewhat bullish stock due to its growing metaverse spending, which is seen as a promising area for future growth.', 'rotation': 'No, do not rotate into Alphabet (GOOGL) as it is a somewhat bearish stock due to the decline in search queries and the increasing competition from AI chatbots.'}TrueTrueTrueTechnology0002025-07-03
662025-07-03 00:00AAPL213.589996001208.50208.894608209.9400020.1889030.68591100206.80207.330093211.1100010.2556762.04159000205.10210.000000211.7899932.3333333.158786006. With Apple (AAPL) launching new Vision Pro features, should I increase my allocation or wait for earnings clarity?\r\nWait for earnings clarity before making any investment decisions. The decline in searches on Safari may impact Apple's business, but the extent of this impact is unclear. Consider increasing your allocation after earnings clarity is provided.FalseFalseFalseTechnology0002025-07-03
772025-07-03 00:00INTU780.799988100814.64787.530029783.5499883.4424053.96784011843.19782.625000769.6900027.7387009.54929901883.49749.393982748.75000017.89392817.995326007. Should I add to my position in Intuit (INTU) before the next tax season, or is most of the growth priced in?\r\nBased on the analysis, it is recommended to add to your position in Intuit (INTU) before the next tax season. The company's strong performance and growth potential make it a good choice to add to your position. However, it's essential to keep an eye on the market and adjust your strategy accordingly.TrueFalseFalseFinTech0102025-07-03
882025-07-03 00:00AVGO275.179993100285.00277.329987274.1799932.7656633.94631510295.00279.290009277.8200075.6249746.18385711310.00274.480011273.82000712.94082913.213057008. Broadcom (AVGO) just completed its VMware integration. Buy on synergy optimism or lock in profits?\r\nBuy on synergy optimismFalseTrueFalseTechnology1002025-07-03
992025-07-03 00:00NFLX1297.1800541001345.001291.3765871289.0699464.1524234.338791001370.001288.2500001288.2500006.3458186.345818001400.001255.1899411249.57495111.53690412.0380970010. Netflix (NFLX) introduced ad‑supported tiers; does that warrant a fresh buy, or should I stick with Paramount Streaming (PARA)?\r\nBased on the analysis, we recommend buying Netflix (NFLX) due to its strong growth prospects and recent introduction of ad-supported tiers. Paramount Streaming (PARA) has a mixed performance, and its current price may not be the best entry point. However, it's essential to monitor PARA's progress and adjust your investment strategy accordingly.FalseFalseFalseMedia Entertainment0002025-07-03
Unnamed: 0Date TimeStockCurr_PriceBuySellHoldDay1_Predclosest_day1_priceactual_day1_closeday1_min_errorday1_act_errormade_profit_could_day_1made_profit_act_day_1Day3_Predclosest_day3_priceactual_day3_closeday3_min_errorday3_act_errormade_profit_act_day_3made_profit_could_day_3Day7_Predclosest_day7_priceactual_day7_closeday7_min_errorday7_act_errormade_profit_could_day_7made_profit_act_day_7QueryAdviceprofit_1profit_3profit_7Categorymissed_opportunity_day1missed_opportunity_day3missed_opportunity_day7Date
1801802025-07-03 00:00BIIB132.860001010132.50132.505005130.1000060.0037771.84473011130.80132.220001132.9400021.0739691.60975101129.10132.710007133.2749942.7202223.1326161094. Should I keep small positions in LRCX, BIIB, and MNST or consolidate into one high-conviction pick?Based on the analysis, we recommend consolidating into one high-conviction pick, LRCX, due to its strong growth prospects and relatively low volatility. The other two stocks, BIIB and MNST, have some concerns with their recent performance and may not be as attractive for investment.TrueFalseFalseHealthcare0112025-07-03
1811812025-07-03 00:00MNST63.08000201063.5063.33000262.8600010.2684321.0181350162.8061.20999959.5400012.5976165.4753091162.1058.79999958.4800005.6122466.1901511194. Should I keep small positions in LRCX, BIIB, and MNST or consolidate into one high-conviction pick?Based on the analysis, we recommend consolidating into one high-conviction pick, LRCX, due to its strong growth prospects and relatively low volatility. The other two stocks, BIIB and MNST, have some concerns with their recent performance and may not be as attractive for investment.TrueTrueTrueConsumer0002025-07-03
1821822025-07-03 00:00ASML794.979980001555.12783.830017784.98999029.17852229.28317500562.15793.205017799.45001229.12929329.68290810573.21796.640015799.20001228.04654728.2770281195. What’s the ideal weight for a 4-stock mix of ASML, KLAC, INTU, and ROST?{'ideal_weight': {'ASML': 30, 'KLAC': 25, 'INTU': 20, 'ROST': 25}, 'reasoning': 'This mix would provide a balanced portfolio with a mix of growth and stability, as well as exposure to different industries.'}FalseTrueTrueTechnology0002025-07-03
1831832025-07-03 00:00KLAC924.659973001345.67905.905029912.90002461.84257962.13495600352.11915.510010923.29998861.53947061.86396600362.45922.099976929.14502060.69298260.9910190195. What’s the ideal weight for a 4-stock mix of ASML, KLAC, INTU, and ROST?{'ideal_weight': {'ASML': 30, 'KLAC': 25, 'INTU': 20, 'ROST': 25}, 'reasoning': 'This mix would provide a balanced portfolio with a mix of growth and stability, as well as exposure to different industries.'}FalseFalseTrueSemiconductors0002025-07-03
1841842025-07-03 00:00INTU780.799988001245.98778.840027783.54998868.41713468.60698101251.23767.010010769.69000267.24553867.35958600258.51743.929993748.67010565.25076265.4707730095. What’s the ideal weight for a 4-stock mix of ASML, KLAC, INTU, and ROST?{'ideal_weight': {'ASML': 30, 'KLAC': 25, 'INTU': 20, 'ROST': 25}, 'reasoning': 'This mix would provide a balanced portfolio with a mix of growth and stability, as well as exposure to different industries.'}TrueFalseFalseFinTech0002025-07-03
1851852025-07-03 00:00ROST131.64999400195.21129.899994130.77000426.70515427.1927840096.58130.690002131.21000726.09993326.3928090098.01129.750000129.86000124.46242824.5264130095. What’s the ideal weight for a 4-stock mix of ASML, KLAC, INTU, and ROST?{'ideal_weight': {'ASML': 30, 'KLAC': 25, 'INTU': 20, 'ROST': 25}, 'reasoning': 'This mix would provide a balanced portfolio with a mix of growth and stability, as well as exposure to different industries.'}FalseFalseFalseConsumer0002025-07-03
1861862025-07-03 00:00EBAY76.38999901074.5075.28869675.8899991.0475631.8315981173.2075.17079976.2799992.6217624.0377541172.5076.33999676.3399965.0301245.0301241199. I’ve held eBay for a while, but I’m considering rotating into either clean energy or industrials. What’s better for 2025?{'coming_day': 'Hold your position in EBAY for the next day as the stock is expected to recover slightly.', 'coming_3_days': 'Consider rotating out of EBAY in the next 3 days as the stock is expected to decline further.', 'coming_week': 'Rotate into clean energy or industrials for the long-term as they are expected to perform better in 2025.', 'reasoning': "The stock's recent underperformance and negative trend indicators suggest a decline in the short-term. However, the company's growth potential and positive news about Louis Vuitton and Birkenstock's performance on the platform suggest that it may be a good long-term investment. Clean energy and industrials are expected to perform better in 2025 due to their growth potential and increasing demand."}TrueTrueTrueConsumer0002025-07-03
1871872025-07-03 00:00ADP308.959991000313.00310.725006308.4899900.7321571.46196300316.00308.612488308.4100042.3937832.46100800320.00305.070007304.1499944.8939565.21124700100. I'm looking at ADP, NTES, and KLAC for long-term growth. Which two have better upside if I can only pick a pair?Based on the analysis, I recommend investing in KLAC and NTES for long-term growth. KLAC has a strong financial performance and a high demand for its stock, while NTES is expanding its reach in the global music industry. ADP is a stable company, but it may not have the same level of growth as KLAC and NTES.TrueTrueTrueTechnology0002025-07-03
1881882025-07-03 00:00NTES132.860001100137.00134.154999133.0399932.1206822.97655411140.00131.050003130.1999976.8294527.52688400144.00128.860001128.25999511.74918512.27195200100. I'm looking at ADP, NTES, and KLAC for long-term growth. Which two have better upside if I can only pick a pair?Based on the analysis, I recommend investing in KLAC and NTES for long-term growth. KLAC has a strong financial performance and a high demand for its stock, while NTES is expanding its reach in the global music industry. ADP is a stable company, but it may not have the same level of growth as KLAC and NTES.TrueFalseFalseTechnology0002025-07-03
1891892025-07-03 00:00KLAC924.659973100930.00922.799988912.9000240.7802351.87314900935.00931.664917923.2999880.3579701.26719501940.00929.255005929.1450201.1563021.16827611100. I'm looking at ADP, NTES, and KLAC for long-term growth. Which two have better upside if I can only pick a pair?Based on the analysis, I recommend investing in KLAC and NTES for long-term growth. KLAC has a strong financial performance and a high demand for its stock, while NTES is expanding its reach in the global music industry. ADP is a stable company, but it may not have the same level of growth as KLAC and NTES.FalseFalseTrueSemiconductors0102025-07-03